Minority Samples Research Articles

Adaptive Oversampling via Density Estimation for Online Imbalanced Classification

Online learning is a framework for processing and learning from sequential data in real time, offering benefits such as promptness and low memory usage. However, it faces critical challenges, including concept drift, where data distributions evolve over time, and class imbalance, which significantly hinders the accurate classification of minority classes. Addressing these issues simultaneously remains a challenging research problem. This study introduces a novel algorithm that integrates adaptive weighted kernel density estimation (awKDE) and a conscious biasing mechanism to efficiently manage memory, while enhancing the classification performance. The proposed method dynamically detects the minority class and employs a biasing strategy to prioritize its representation during training. By generating synthetic minority samples using awKDE, the algorithm adaptively balances class distributions, ensuring robustness in evolving environments. Experimental evaluations across synthetic and real-world datasets demonstrated that the proposed method achieved up to a 13.3 times improvement in classification performance over established oversampling methods and up to a 1.66 times better performance over adaptive rebalancing approaches, while requiring significantly less memory. These results underscore the method’s scalability and practicality for real-time online learning applications.

RECOMMENDATIONS TO IMPROVE AGING PAIN INTERVENTION RESEARCH ON MINORITIES: A SCOPING REVIEW

Abstract Limited information is available for evidence supported psychological treatment options for pain in minority populations. The purposes of this scoping review were to determine if psychological treatments were 1) evaluated for minority populations; 2) if minorities were sufficiently included in clinical trials 3) if there were trials that were specific to minority populations. A search was conducted in PsycINFO, MEDLINE, and CINAHL databases for psychological interventions for pain in older adults over the age of 50. We also searched review articles. Search terms were chronic pain OR pain; older adults OR elderly OR geriatric OR aged OR human; pain management OR nonpharmacological pain management OR intervention OR psychotherapy; randomized control trial. Papers had to be in English, a randomized clinical trial, with pain as the primary outcome, and the treatment had to be a psychological intervention. We coded studies that had at least a 90% minority sample as a trial specific to a minority population or if one of the groups was minority. From 14,000 studies identified by the search, we reviewed 485 full texts. Of those,129 psychological interventions studies on older adults with chronic pain met inclusion and exclusion criteria. There were 6 African-American, 7 Asian,1 Turkish, and 2 Iranian samples. Studies represented 24 countries. However, 72 studies did not provide enough information about ethnicity or race. The lack of reporting of race and ethnicity took place across many disciplines and journal types. All journals should require reporting ethnic and racial demographics of participants to increase generalizability of findings.

Three limited interaction approaches to understanding the epidemiology of HIV among YMSM in the US

BackgroundUsing a theoretically-grounded approach to the epidemiological study of HIV incidence among a national, diverse sample of sexual and gender minority (SGM) men (age 17 -29 years), as well as examining HIV incidence through an innovative geospatial lens, is of considerable public health significance. Our overarching objectives are to assemble a U.S.-based national cohort of diverse SGM men: (1) to estimate HIV incidence in SGM men followed every 6 months for up to 24 months, (2) to assess the association of individual and geospatial factors associated with HIV incidence and (3) to determine the relative efficiency and acceptability of three different, discrete study enrollment approaches (including completion of remote HIV testing). The purpose of this manuscript is to describe the study protocol.MethodsThe cohort is composed of English- and/or Spanish-speaking SGM men at risk for HIV, age 17–29 years and living in the United States and its territories. We used multiple methods to recruit our sample including social networking apps like GrindrTM. If a participant was eligible for the study, they completed an address intake form so an HIV test could be mailed to their home or chosen address. We assembled three cohorts using different enrollment approaches. Cohort 1 used Zoom video calls with study staff observing participants use of OraQuick test with oral swabs at the baseline visit. Cohort 2 used No Zoom and OraSure oral fluid tests that participants mailed to an external lab. Cohort 3 used No Zoom/self-administration of OraQuick tests and participants uploading test results to an online portal (REDCap).DiscussionThis study will provide important data on multilevel determinants of HIV incidence among SGM men at the national level, allowing us to examine important differences by local jurisdiction, region and state and to better understand the impact of individual, social and geospatial factors on HIV incidence to help inform future prevention strategies.

Non-negligible prevalence of focal lymphocytic sialadenitis in minor salivary glands of non-Sjögren's disease individuals.

Focal lymphocytic sialadenitis (FLS) in minor salivary gland biopsy (MSGB) has long been regarded as a histologic hallmark of Sjögren's disease (SjD), but it can also occur in non-SjD individuals. This study aimed to define the prevalence of FLS in labial minor salivary glands of non-SjD individuals via both an autopsy study and a meta-analysis. A total of 214 genotype-tissue expression (GTEx) volunteers was included in the autopsy study, and FLS in labial minor salivary gland was evaluated. A meta-analysis was also performed to comprehensively define the prevalence of FLS in labial minor salivary glands of non-SjD individuals. In the autopsy study of 214 GTEx volunteers, the frequency of FLS in labial minor salivary glands was 13.1%. GTEx volunteers aged 60 years and older demonstrated a greater prevalence of FLS compared to those volunteers younger than 60 years (20.3% vs. 9.7%, p=0.03). In the meta-analysis, a total of 8 eligible studies involving 917 labial minor salivary gland samples were included. The pooled prevalence of FLS in labial minor salivary glands of non-SjD individuals was 6.2%. In the subgroup analysis by gender, the pooled prevalence of FLS in labial minor salivary glands for female and male non-SjD individuals was 10.4% and 5.0%, respectively. This study provides compelling epidemiological evidence for the considerably high prevalence of FLS in minor salivary glands of non-SjD individuals. The clinical significance of FLS should be cautiously considered when MSGB is used to confirm seronegative SjD.

Recommendation for Clarifying FDA Policy in Evaluating "Sameness" of Higher Order Structure for Generic Peptide Therapeutics.

Recognizing the approach of a dramatic expansion of peptide therapeutics reaching the marketplace in recent years, led by GLP-1 receptor agonists such as semaglutide and liraglutide, the Center for Drug Evaluation and Research (CDER) branch of the US Food and Drug Administration (FDA) issued a final guidance in 2021 that was intended to assist generic drug producers in meeting Abbreviated New Drug Application (ANDA) obligations to establish "sameness" of their active peptide drug relative to that produced by innovator companies. Research and a published report by FDA scientists on best practices followed, which promulgated the use of nuclear magnetic resonance (NMR) and principal component analysis (PCA) and established a quantitative standard by which "sameness" of higher order structure for the applicant's peptide drug could be judged. A key requirement is that drug product samples be analyzed directly and non-invasively, a condition which in practice restricts sample modification to the addition of a small amount of deuterium oxide to allow signal lock and spectral data alignment (as required for NMR analysis). In the study described herein, data are presented to illustrate that 1) relatively small differences in sample pH can cause significant shifting of certain proton resonances, 2) that such resonance shifting is readily reversible and due to the degree of protonation of specific amino acid residues (rather than reflecting differences in higher order structure), and 3) that small differences in pH variability between sample cohorts can frequently cause failure to meet the quantitative benchmark established by the agency. Methodology is presented by which drug sample pHs can be aligned with minimal impact, and a recommendation is made that minor sample pH adjustments be allowed in assessing "sameness" of peptide drug higher order structure.

Stigma and Sexual Dissatisfaction in Middle-Aged and Older Sexual Minorities.

High sexual quality and activity predict psychological well-being in heterosexual middle-aged and older adults; however, these associations have not been documented in sexual minorities, who have faced lifelong stigma concerning their sexuality. This paper presents data from two secondary studies that explored the benefits of being sexually active and satisfied and the role of internalized homonegativity in a sample of middle-aged and older sexual minorities. Study 1 was a cross-sectional survey collected on 91 sexually active sexual minorities (ages 40-80) in same-sex relationships. Study 2 was a cross-sectional survey collected on 235 single and partnered sexual minorities (ages 40-90), which included sexually active (N = 101) and inactive (N = 134) participants. In Study 1, sexual satisfaction was associated with higher psychological well-being and dissatisfaction was more prominently associated with lower psychological well-being. In Study 2, we found that the sexually active participants had higher levels of psychological well-being, in addition to corroborating the Study 1 findings. Additionally, we also determined that sexual dissatisfaction was a mediator between internalized homonegativity and psychological well-being. These findings accentuate the benefits of retaining high sexual quality and activity for middle-aged and older sexual minorities. Although there are apparent advantages of being sexually satisfied, the relationship to psychological well-being is stronger and more of a concern for those who are dissatisfied, which is rooted in internalized homonegativity. These results can guide practitioners working with middle-aged and older sexual minorities to help them achieve successful aging trajectories by reducing their internalized homonegativity and promoting higher sexual quality.

Adaptive weights-based relaxed broad learning system for imbalanced classification

As a shallow neural network, broad learning system (BLS) has gained significant attention in both academia and industry due to its efficiency and effectiveness. However, BLS and its variants are suboptimal when confronted with imbalanced data scenarios. Firstly, strict binary labeling strategy hinders effective disparities between different classes. Secondly, they generally do not distinguish between the contributions of minority and majority classes, resulting in classification outcomes biased toward the majority classes. To address these deficiencies, we propose an adaptive weights-based relaxed broad learning system for handling imbalanced classification tasks. We provide a label relaxation technique to construct a novel label matrix that not only widens the margins between classes but also maintains label consistency within each class. Additionally, an adaptive weighting strategy assigns higher weights to minority samples based on density information within and between classes. This enables the model to learn a more discriminative transformation matrix for imbalanced classification. The alternating direction method of multipliers algorithm is employed to solve the resulting model. Experimental results on numerous public imbalanced data sets demonstrate the effectiveness and efficiency of the proposed method.

Metal–Organic Framework-Derived CeO2/Gold Nanospheres in a Highly Sensitive Electrochemical Sensor for Uric Acid Quantification in Milk

In this work, CeBTC (a cerium(III) 1,3,5-benzene-tricarboxylate), was used as a precursor for obtaining CeO2 nanoparticles (nanoceria) with better sensor performances than CeO2 nanoparticles synthesized by the solvothermal method. Metal–organic framework-derived nanoceria (MOFdNC) were functionalized with spheric gold nanoparticles (AuNPs) to further improve non-enzymatic electrode material for highly sensitive detection of prominent biocompound uric acid (UA) at this modified carbon paste electrode (MOFdNC/AuNPs&CPE). X-ray powder diffraction (XRPD) and transmission electron microscopy (TEM) analysis were used for morphological structure characterization of the obtained nanostructures. Cyclic voltammetry and electrochemical impedance spectroscopy, both in an [Fe(CN)6]3−/4− redox system and uric acid standard solutions, were used for the characterization of material electrocatalytic performances, the selection of optimal electrode modifier, and the estimation of nature and kinetic parameters of the electrode process. Square-wave voltammetry (SWV) was chosen, and the optimal parameters of technique and experimental conditions were established for determining uric acid over MOFdNC/AuNPs&CPE. Together with the development of the sensor, the detection procedure was optimized with the following analytical parameters: linear operating ranges of 0.05 to 1 µM and 1 to 50 µM and a detection limit of 0.011 µM, with outstanding repeatability, reproducibility, and stability of the sensor surface. Anti-interference experiments yielded a stable and nearly unchanged current response with negligible or no change in peak potential. After minor sample pretreatment, the proposed electrode was successfully applied for the quantification of UA in milk.

Prey selection by Chordeiles minor (Common Nighthawk) does not reflect differences in prey availability between breeding and nonbreeding grounds

Abstract Predators may adjust their diets to match their energy needs and food availability, but these adjustments have not been explored for migratory aerial insectivores outside of the breeding grounds. We found that Chordeiles minor (Common Nighthawk), a long-distance migrant and member of the rapidly declining aerial insectivore guild, exhibited similar levels of diet richness, diet diversity, and prey selectivity on the breeding and nonbreeding grounds, despite large differences in prey abundance. We examined the diets and prey communities of C. minor during 2 breeding seasons in Florida, USA, and 2 nonbreeding seasons in Corrientes Province, Argentina (2020 to 2022). We used DNA metabarcoding to identify insect prey in C. minor fecal samples, and we employed Malaise and UV light traps to assess abundance and composition of aerial insect prey communities. Abundance and richness of available prey were significantly higher on the nonbreeding grounds than on the breeding grounds. Even so, C. minor exhibited similar within-sample and within-population diet richness, Shannon and Simpson diversities, and prey preferences at both sites. Adults differed in their consumption of Lepidoptera between sites: adults on the nonbreeding grounds preferred Lepidoptera over all other orders, whereas adults on the breeding grounds consumed Lepidoptera less frequently than expected. We suggest that breeding adult C. minor may deliver Lepidoptera to their young instead of consuming this prey. At both sites, C. minor showed preference for Hemiptera and Hymenoptera–both large-bodied, nutrient-rich prey–suggesting that these generalist predators exhibit less diet flexibility than previously thought and thus may be vulnerable to changes in prey communities at multiple points in the annual cycle.

MinoritySalMix and adaptive semantic weight compensation for long-tailed classification

In real-world datasets, the widespread presence of a long-tailed distribution often leads models to become overly biased towards majority class samples while ignoring minority class samples. We propose a strategy called MASW (MinoritySalMix and adaptive semantic weight compensation) to improve this problem. First, we propose a data augmentation method called MinoritySalMix (minority-saliency-mixing), which uses significance detection techniques to select significant regions from minority class samples as cropping regions and paste them into the same regions of majority class samples to generate brand new samples, thereby amplifying images containing important regions of minority class samples. Second, in order to make the label value information of the newly generated samples more consistent with the image content of the newly generated samples, we propose an adaptive semantic compensation factor. This factor provides more label value compensation for minority samples based on the different cropping areas, thereby making the new label values closer to the content of the newly generated samples. Improve model performance by generating more accurate new label value information. Finally, considering that some current re-sampling strategies generally lack flexibility in handling class sampling weight allocation and frequently require manual adjustment. We designed an adaptive weight function and incorporated it into the re-sampling strategy to achieve better sampling. The experimental results on three long-tailed datasets show that our method can effectively improve the performance of the model and is superior to most advanced long-tailed methods. Furthermore, we extended MinoritySalMix’s strategy to three balanced datasets for experimentation, and the results indicated that our method surpassed several advanced data augmentation techniques.

An advancement in AdaSyn for imbalanced learning: An application to fraud detection in digital transactions

Imbalanced Learning is a significant issue in machine learning, affecting the performance and accuracy of binary or multi-classification algorithms, especially in large-scale data handling and classification. There are some popular techniques to covert this imbalanced data into a balanced one such as undersampling, under-sampling with tomek links, randomized oversampling, synthetic minority oversampling technique (SMOTE), and adaptive synthetic generation (ADASYN). Generally, the ADASYN algorithm could be used to propagate minority sample points to rise the imbalanced ratio between majority and minority sample points, but in some cases, it may conflict with decision boundary points and noisy points. This paper proposed a Refitted AdaSyn Algorithm (RAA) with Gaussian Distribution (GD). So that new minority samples are distributed much closer to the center of the minority sample to spotlight the conflicts. The classification accuracy has improved with RAA over formal ADASYN. For examining the proposed work the imbalanced benchmark datasets like European, Banksim, Paymentcard, and UCI credit card are considered. Vanilla Generative Adversarial Network (GAN) is a deep learning model used to classify fraud and non-fraud transactions, demonstrating significant differences between balanced and imbalanced learning approaches and achieving an accuracy of 97.5% on dataset DS4.

SGO: An innovative oversampling approach for imbalanced datasets using SVM and genetic algorithms

Imbalanced datasets present a challenging problem in machine learning and artificial intelligence. Since most models typically assume balanced data distributions, imbalanced positive and negative examples can lead to significant bias in prediction or classification tasks. Current over-sampling methods frequently encounter issues like overfitting and boundary bias. A novel imbalanced data augmentation technique called SVM-GA over-sampling (SGO) is proposed in this paper, which integrates Support Vector Machines (SVM) with Genetic Algorithms (GA). Our approach leverages SVM to identify the decision boundary and uses GA to generate new minority samples along this boundary, effectively addressing both over-fitting and boundary biases. It has been experimentally validated that SGO outperforms the traditional methods on most datasets, providing a novel and effective approach to address imbalanced data problems, with potential application prospects and generalization value.

A novel generalised extreme value gradient boosting decision tree for the class imbalanced problem in credit scoring

The performance of credit scoring models can be compromised when dealing with imbalanced datasets, where the number of defaulted borrowers is significantly lower than that of non-defaulters. To address this challenge, we propose a gradient boosting decision tree with the generalised extreme value distribution model (GEV-GBDT). Our approach replaces the conventional symmetric logistic sigmoid function with the asymmetric cumulative distribution function of the GEV distribution as the activation function. We derive a novel loss function based on the maximum likelihood estimation of the GEV distribution within the boosting framework. This modification allows the model to focus more on the minority class by emphasising the tail of the response curve, and the shape parameter of the GEV distribution offers flexibility in controlling the model’s emphasis on minority samples. We examine the performance of this approach using four real-life loan datasets. The empirical results show that the GEV-GBDT model achieves superior classification performance compared to other commonly used imbalanced learning methods, including the synthetic minority oversampling technique and the cost-sensitive framework. Furthermore, we conduct performance tests on several datasets with varying imbalance ratios and find that GEV-GBDT performs better on extremely imbalanced datasets.

New Method for Tomato Disease Detection Based on Image Segmentation and Cycle-GAN Enhancement

A major concern in data-driven deep learning (DL) is how to maximize the capability of a model for limited datasets. The lack of high-performance datasets limits intelligent agriculture development. Recent studies have shown that image enhancement techniques can alleviate the limitations of datasets on model performance. Existing image enhancement algorithms mainly perform in the same category and generate highly correlated samples. Directly using authentic images to expand the dataset, the environmental noise in the image will seriously affect the model’s accuracy. Hence, this paper designs an automatic leaf segmentation algorithm (AISG) based on the EISeg segmentation method, separating the leaf information with disease spot characteristics from the background noise in the picture. This algorithm enhances the network model’s ability to extract disease features. In addition, the Cycle-GAN network is used for minor sample data enhancement to realize cross-category image transformation. Then, MobileNet was trained by transfer learning on an enhanced dataset. The experimental results reveal that the proposed method achieves a classification accuracy of 98.61% for the ten types of tomato diseases, surpassing the performance of other existing methods. Our method is beneficial in solving the problems of low accuracy and insufficient training data in tomato disease detection. This method can also provide a reference for the detection of other types of plant diseases.

High vs. Low Intensity Behavior Therapy Delivered to Adolescents with ADHD: Potential Adverse Long-Term Effects on Substance Use Outcomes.

Individuals with ADHD are at risk for substance use initiation in adolescence and escalation to problematic use in adulthood. Little is known about the impact of psychosocial ADHD treatment on substance use. Based on existing theory, both therapeutic (i.e., through reducing symptoms and impairments) and iatrogenic effects (i.e., through improved social functioning) of psychosocial treatment for ADHD on adolescent substance use initiation are plausible. A primarily ethnic/racial minority sample (~ 95% Latinx or Black) of rising ninth grade students with ADHD (n = 106) were randomly assigned to receive high intensity (i.e., Summer Treatment Program-Adolescent, parent training, and school consultation) or lower intensity (parent training, organization skills training, and school consultation) intervention the summer before entering high school. Participants were followed four-years post-baseline and substance use was documented. Analyses tested treatment effects on substance use initiation (alcohol and/or marijuana) and mediators of main effects. After controlling for covariates, participants assigned to HI (37.5%) were significantly more likely than LI (18.6%) to initiate substance use by end of high school, indicating an iatrogenic effect of HI treatment. No significant mediators were detected. Post-hoc exploration of moderators suggested that youth with elevated Posttraumatic Stress Disorder (PTSD) symptoms may have experienced a benefit of HI treatment on substance use whereas youth without elevated PTSD symptoms experienced iatrogenic effects. Large, well-powered, samples should examine moderated mediational models to better understand who is most risk for iatrogenic effects of ADHD psychosocial treatment and why. Clinicians delivering psychosocial treatment to adolescents with ADHD should monitor for potential iatrogenic effects.

Mixed Mutual Transfer for Long-Tailed Image Classification.

Real-world datasets often follow a long-tailed distribution, where a few majority (head) classes contain a large number of samples, while many minority (tail) classes contain significantly fewer samples. This imbalance creates an information disparity between head and tail classes, which can negatively impact the performance of deep networks. Some transfer knowledge techniques attempt to mitigate this gap by generating additional minority samples, either through oversampling the tail classes or transferring knowledge from the head classes to the tail classes. However, these methods often restrict the diversity of the generated minority samples, primarily focusing on transferring information only to the tail classes. This paper introduces a simple yet effective method for long-tailed classification, called mixed mutual transfer (MMT), which facilitates the mutual transfer of knowledge between head and tail classes by blending samples. The core idea of our method is to create new samples by blending head and tail samples. Head samples are selected using a uniform sampler that retains the long-tailed distribution, while tail samples are selected using a differential sampler that reverses the long-tailed distribution to alleviate imbalance. Our approach aims to diversify both tail and head classes. During the training phase, we utilize the generated samples to update the original dataset for training deep networks. Mixed mutual transfer simultaneously enhances the performance of both head and tail classes. Experimental results on various class-imbalanced datasets show that the proposed method significantly outperforms existing methods, demonstrating its effectiveness in improving the performance of long-tailed deep networks.

An empirical investigation of ethnic-cultural procedural fairness effects and their boundary conditions among three societal groups in South Africa

ABSTRACT Ethnic-cultural procedural fairness refers to the perception that societal actors use fair and unbiased procedures to make decisions vis-à-vis one’s ethnic-cultural group. Prior research using ethnic-cultural minority samples has convincingly shown that such perceptions are associated with a range of desirable outcomes. Yet, this body of evidence has so far limited its scope to European and North American samples. Moreover, more research is needed on the underlying psychological processes. Therefore, the central aim of the present study was to investigate ethnic-cultural procedural fairness effects among three societal groups in South Africa. In Study 1 (N = 326 black South Africans), we observed relationships between ethnic-cultural fairness perceptions and positive institutional and intergroup attitudes. Institutional and social trust respectively mediated these associations. Study 2 (N = 747) replicated these findings among white (n = 595), but not among coloured South Africans (n = 152). Notably, across both studies, we also obtained evidence for moderation. That is, procedural fairness effects on institutional attitudes were significantly smaller among socioeconomically burdened black participants, and procedural fairness effects on intergroup attitudes were fully curbed among white participants who reported high levels of perceived group discrimination. Taken together, our results call for enhanced attention to procedural fairness obstacles outside of typical WEIRD settings.

PDSMV3-DCRNN: A novel ensemble deep learning framework for enhancing phishing detection and URL extraction

Phishing is a cyber-attack that exploits victims' technical ignorance or naivety and commonly involves a Uniform Resources Locator (URL). As a result, it is beneficial to examine URLs before accessing them to spot a phishing assault. Several algorithms based on machine learning have been presented to detect phishing attempts. However, these approaches often suffer from lower performance outcomes, such as lower accuracy, longer response times, and higher false positive rates. Furthermore, many existing methods rely heavily on predefined feature sets, which may limit their adaptability and robustness. In contrast, our proposed method leverages a more dynamic feature selection process, which includes the Conditional Wasserstein Generative Adversarial Network (CWGAN) for addressing data imbalance and the Binary Grey Goose Optimization Algorithm (BGGOA) for optimal feature selection. This dynamic approach enhances the model's ability to adapt to varying data characteristics, improving detection performance. The proposed solution is divided into two stages: pre-deployment and deployment. During the pre-deployment stage, the dataset is preprocessed, including data transformation, handling irrelevant and redundant data, and ensuring data balancing. Minority samples are increased using CWGAN to avoid class imbalance. Features are then selected using BGGOA, resulting in a feature-reduced dataset used for training and testing ensemble deep learning classifiers, specifically the Novel Pyramid Depth-wise Separable-MobileNetV3 (PyDS-MV3) and Deformable Convolutional Residual Neural Network (DCRNN), termed PDSMV3-DCRNN. During the deployment phase, the Boosted ConvNeXt approach extracts URL features fed into the trained classifier to predict "phishing" or "benign". According to experimental findings, the proposed solution outperforms all other tested approaches, displaying a faster training time of 0.11 s and achieving an optimal accuracy of 99.21%.

Patient comfort with sexual orientation and gender identity questions in adult congenital cardiology clinics.

Lack of sexual orientation and gender identity (SOGI) data creates barriers for lesbian, gay, bisexual, transgender, and queer (LGBTQ+) people in health care. Barriers to SOGI data collection include physician misperception that patients do not want to answer these questions and discomfort asking SOGI questions. This study aimed to assess patient comfort towards SOGI questions across five quaternary care adult congenital heart disease (ACHD) centres. A survey administered to ACHD patients (≥18 years) asked (1) two-step gender identity and birth sex, (2) acceptance of SOGI data, and (3) the importance for ACHD physicians to know SOGI data. Chi-square tests were used to analyse differences among demographic groups and logistic regression modelled agreement with statement of patient disclosure of SOGI improving patient-physician communication. Among 322 ACHD patients, 82% identified as heterosexual and 16% identified as LGBTQ+, across the age ranges 18-29 years (39.4%), 30-49 years (47.8%), 50-64 years (8.7%), and > 65 years (4.0%). Respondents (90.4%) felt comfortable answering SOGI questions. Respondents with bachelor's/higher education were more likely to "agree" that disclosure of SOGI improves patient-physician communication compared to those with less than bachelor's education (OR = 2.45; 95% CI 1.41, 4.25; p = .0015). These findings suggest that in this largely heterosexual population, SOGI data collection is unlikely to cause patient discomfort. Respondents with higher education were twice as likely to agree that SOGI disclosure improves patient-physician communication. The inclusion of SOGI data in future studies will provide larger samples of underrepresented minorities (e.g. LGBTQ+ population), thereby reducing healthcare disparities within the field of cardiovascular research.

Assessment of the impact of ethnic minorities’ capacity in Binh Dinh province, Vietnam, to access land and exercise land use rights by application of linear structure model (PLS-SEM)

Abstract The study aimed to evaluate the impact of ethnic minority people’s capacity on land access and exercise of land use rights. The research model and hypotheses were tested using exploratory factor analysis (EFA) and PLS-SEM methods with 70 preliminary quantitative survey samples and 273 official quantitative survey samples of ethnic minorities. The research results revealed that strengthening the capacity of ethnic minorities is the key to improving their effectiveness in accessing land and exercising land use rights. The research findings demonstrate a robust and affirmative correlation between the capacity of ethnic minorities to acquire land access and the implementation of land use rights (βCO→LA = 0.325; βCO→LUR = 0.736; estimated path coefficients close to +1 represent strong positive relationships). The research model attains a significance level of 1% (tCO→LA = 8.412; tCO→LUR = 42.643). The outcomes of this research can serve as a foundation for the People’s Committee of Binh Dinh Province, Committee for Ethnic Minority, to further establish policies, support programs, and training to enhance the capacity of ethnic minorities, and to provide guidance on improving the effectiveness of land access and exercise of land use rights for this community.